1. Field of the Invention
This invention relates to the use of pattern recognition methodologies for developing a system for automatic categorization of a person from his/her image into a particular age category.
2. Background of the Invention
Age Classification has numerous applications and has the potential of not only enhancing the existing HCI system but can also serve as platform for passive surveillance (for e.g., alerting medical authorities if there is an accident in old age home). It can also be used for development of new HCI application (e.g., cigarette vending machines based on age verification), immersive computer games (for e.g., changing scenarios and multimedia content based on age category preferences), collecting retail business information (e.g., the number of children entering a retail store on a given day), image retrieval (for e.g., accessing all images belonging to babies), enhancing identity verification, and advertising (for e.g., focusing on a particular age group for selling a product).
To date there has been only two attempt to classify a person in an age category just from the facial information. U.S. Pat. No. 5,781,650 to De Lobo describes an automatic feature detection and age classification method for human face in images. Their automatic age categorization system is based on finding a face in an image and locating the facial features such as nose, eyes, mouth, top of the head and chin. These features were then used to determine different T ratios (transverse ratios of distance between eyes to the distance between line connecting the eyes from the nose/chin/top of head) that were then used for classification purposes. In the paper titled “Age Classification for Facial Images” by the same inventors Young H. Kwon and Niels De Vitoria Lobo, Computer Vision and Image Understanding, 74(1), pp. 1-21, 1991, they described their above patented method based on cranio-facial development theory and wrinkle analysis for age classification. In their invention, they did not use direct appearance information available from a face image to classify, instead they used geometric ratios obtained from the position of the facial features and presence of wrinkles.
Patent Application No. 60/421,717 by R. Sharma, M. Yeasin, and R. Khare uses direct appearance information for classifying humans into two age categories. Appearance information is used to extract discriminating features and these features used to train a bank of classifiers to derive the binary age class of the person. They do not have a method for dividing people in more than two classes (multiple classes) from appearance-based information.
Patent granted to Michael J. Jones, U.S. Pat No. (Application) US20020102024A1, describes a method for object detection using integral image representation of the input image. The object detector uses a cascade of homogenous classification functions or classifiers. Their invention defines a fast method for object detection using rectangular components defined by wavelets. The research paper titled “A Unified Learning Framework for Real Time Face Detection & Classification”, Gregory Shakhnarovich, Michael J. Jones, and Baback Moghaddam, International Conference on Automatic Face and Gesture Recognition, 2002, performed gender and ethnicity classification using integral image. It calculates the integral image rather than classifying on basis of the face appearance. Furthermore, their system does not perform age classification.
U.S. Pat. No. 5,963,670 to P. R. Lipson et. al., describes a method for classifying and detecting objects in images using a class model based on global deformable templates. This method is based on building a class model in terms of a deformable template and cannot be applied for age classification.
In Andreas Lanitis, Chris J. Taylor and Timothy F. Cootes, “Towards Automatic Simulation of Aging Effects on Face Images”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, No. 4, April 2002, some work has been done in the field of simulating aging in facial images. In that paper the main aim was to make face recognition robust with respect to aging variations. Thus given the face of a person the face is “age normalized” before being used in face recognition. This method can be used for simulating ageing effects but does not address age classification.
In D. Micheal Burt, and David. I. Perrett, “Perception of age in adult Caucasian male faces: computer graphic manipulation of shape and colour information”, Proceedings of the Royal Society of London, Vol. 259, pp 137-143, 1995, a study was performed on the correlation between the perceived age and the chronological age. Though this study provides an insight into the ageing process, it does not deal with age classification of digital face images.
Patent granted to Player, U.S. Pat No. (Application) US20020052881A1, shows an example of use of demographic information for customizing computer games and advertising. They did not show any method or system for extracting demographic information from images or videos.